A content-enhanced approach for cold-start problem in collaborative filtering

Recommender systems are widely used in online business to satisfy user personalization demands. The most successful technique of such systems is collaborative filtering, which utilizes users' known preference to generate predictions of the unknown preferences. A key challenge for collaborative filtering recommender systems is providing high quality recommendations to new users that have not enough known preferences. In this paper, we propose a hybrid algorithm by using both the ratings and content information to tackle user-side cold-start problem. We first cluster users based on their interests and then utilize the clustering results and users' demographic information to build a decision tree to associate the novel users with the existing ones. Considering the novel user's ratings constantly increasing, we make predictions for novel users by combining our method with the collaborative filtering algorithm. Experiments on real data set show the improvement of our approach in overcoming the user-side cold-start problem.